Open Access

ARTICLE

Exploring the Approaches to Data Flow Computing

by Mohammad B. Khan¹, Abdul R. Khan^2,*, Hasan Alkahtani²

1 Department of Electrical and Computer Engineering, Technical University of Munich, Arcisstrasse 21, 80333 Munich, Germany
2 Department of Computer Science, CCSIT, King Faisal University, 31982, Al Ahsa, KSA

* Corresponding Author: Abdul R. Khan. Email:

(This article belongs to the Special Issue: Pervasive Computing and Communication: Challenges, Technologies & Opportunities)

Computers, Materials & Continua 2022, 71(2), 2333-2346. https://doi.org/10.32604/cmc.2022.020623

Received 31 May 2021; Accepted 03 September 2021; Issue published 07 December 2021

Abstract

Architectures based on the data flow computing model provide an alternative to the conventional Von-Neumann architecture that are widely used for general purpose computing. Processors based on the data flow architecture employ fine-grain data-driven parallelism. These architectures have the potential to exploit the inherent parallelism in compute intensive applications like signal processing, image and video processing and so on and can thus achieve faster throughputs and higher power efficiency. In this paper, several data flow computing architectures are explored, and their main architectural features are studied. Furthermore, a classification of the processors is presented based on whether they employ either the data flow execution model exclusively or in combination with the control flow model and are accordingly grouped as exclusive data flow or hybrid architectures. The hybrid category is further subdivided as conjoint or accelerator-style architectures depending on how they deploy and separate the data flow and control flow execution model within their execution blocks. Lastly, a brief comparison and discussion of their advantages and drawbacks is also considered. From this study we conclude that although the data flow architectures are seen to have matured significantly, issues like data-structure handling and lack of efficient placement and scheduling algorithms have prevented these from becoming commercially viable.

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